In [6]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
In [5]:
images=np.load("predict_image_epoch150.npy")

0~100

In [74]:
start=0
goal=start+100
hoge= np.sqrt(len(images[0,0,start:goal]))
plt.figure(figsize=(15, 15), dpi=800)
for i in xrange(len(images[0,0,start:goal])):
    plt.subplot(hoge+1,hoge+1 , i+1)
    fig = plt.imshow(images[:,:,start+i])
    plt.axis('off')
In [91]:
plt.figure()
plt.imshow(world,"gray")
plt.scatter(trajectory[0,0:100]-14,trajectory[1,0:100]-14,color='g')
plt.axis('off')
plt.show()

4000~4100

In [94]:
start=4000
goal=start+100
hoge= np.sqrt(len(images[0,0,start:goal]))
plt.figure(figsize=(15, 15), dpi=800)
for i in xrange(len(images[0,0,start:goal])):
    plt.subplot(hoge+1,hoge+1 , i+1)
    fig = plt.imshow(images[:,:,start+i])
    plt.axis('off')
In [95]:
plt.figure()
plt.imshow(world,"gray")
plt.scatter(trajectory[0,4000:4100]-14,trajectory[1,4000:4100]-14,color='g')
plt.axis('off')
plt.show()

8000~8100

In [73]:
start=8000
goal=start+100
hoge= np.sqrt(len(images[0,0,start:goal]))
plt.figure(figsize=(15, 15), dpi=800)
for i in xrange(len(images[0,0,start:goal])):
    plt.subplot(hoge+1,hoge+1 , i+1)
    fig = plt.imshow(images[:,:,start+i])
    plt.axis('off')
In [92]:
plt.figure()
plt.imshow(world,"gray")
plt.scatter(trajectory[0,8000:8100]-14,trajectory[1,8000:8100]-14,color='g')
plt.axis('off')
plt.show()

12000~12100

In [75]:
start=12000
goal=start+100
hoge= np.sqrt(len(images[0,0,start:goal]))
plt.figure(figsize=(15, 15), dpi=800)
for i in xrange(len(images[0,0,start:goal])):
    plt.subplot(hoge+1,hoge+1 , i+1)
    fig = plt.imshow(images[:,:,start+i])
    plt.axis('off')
In [93]:
plt.figure()
plt.imshow(world,"gray")
plt.scatter(trajectory[0,12000:12100]-14,trajectory[1,12000:12100]-14,color='g')
plt.axis('off')
plt.show()
In [15]:
trajectory=np.load("trajectory_epoch150.npy")
In [22]:
i=1000
plt.plot(trajectory[0,:i],trajectory[1,:i])
plt.xlim([0, 28*3])
plt.ylim([0,28*3])
Out[22]:
(0, 84)
In [77]:
world=np.load("world1.npy")
In [78]:
plt.imshow(world)
Out[78]:
<matplotlib.image.AxesImage at 0x13adc6fd0>
In [89]:
plt.figure()
plt.imshow(world,"gray")
plt.scatter(trajectory[0,:i]-14,trajectory[1,:i]-14,color='g')
plt.axis('off')
plt.show()
In [60]:
=4000

plt.scatter(trajectory[0,-i:],trajectory[1,-i:], )
#plt.xlim([0, 28*3])
#plt.ylim([0,28*3])
Out[60]:
<matplotlib.collections.PathCollection at 0x112ce2ed0>
In [56]:
recoloss=np.load("recon_loss_epoch150.npy")
In [57]:
plt.plot(recoloss)
Out[57]:
[<matplotlib.lines.Line2D at 0x1145ce190>]
In [58]:
recoloss.shape
Out[58]:
(149,)
In [97]:
fig = plt.figure()
plt.axis('off')
ims = []
for i in range(100):
    im = plt.scatter(trajectory[0,:i]-14,trajectory[1,:i]-14,color='g')             
    ims.append(im)                  


ani = animation.ArtistAnimation(fig, ims, interval=10)
ani.save("output.gif", writer="imagemagick")

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-97-90992cf42f9b> in <module>()
      8 
      9 ani = animation.ArtistAnimation(fig, ims, interval=10)
---> 10 ani.save("output.gif", writer="imagemagick")

/Users/tsuzuki/.conda/envs/py27/lib/python2.7/site-packages/matplotlib/animation.pyc in save(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs)
    830                 for anim in all_anim:
    831                     # Clear the initial frame
--> 832                     anim._init_draw()
    833                 for data in zip(*[a.new_saved_frame_seq()
    834                                   for a in all_anim]):

/Users/tsuzuki/.conda/envs/py27/lib/python2.7/site-packages/matplotlib/animation.pyc in _init_draw(self)
   1098         figs = set()
   1099         for f in self.new_frame_seq():
-> 1100             for artist in f:
   1101                 artist.set_visible(False)
   1102                 artist.set_animated(self._blit)

TypeError: 'PathCollection' object is not iterable
In [ ]: